ArticleRoyal Society open science2018
Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity.
Article in Royal Society open science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- SuperARC: a test for artificial superintelligence based on compressed modelling, recursive prediction and problem complexity.Nature communications · 2026Article
- Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis.Frontiers in computational neuroscience · 2026Article
- The future of fundamental science led by generative closed-loop artificial intelligence.Frontiers in artificial intelligence · 2026Review
- Approaching the general quantification of functional information.Scientific reports · 2025Article
- A Metric for the Entropic Purpose of a System.Entropy (Basel, Switzerland) · 2025Article
- On the salient limitations of the methods of assembly theory and their classification of molecular biosignatures.NPJ systems biology and applications · 2024Review
- Molecular assembly indices of mineral heteropolyanions: some abiotic molecules are as complex as large biomolecules.Journal of the Royal Society, Interface · 2024Article
- Emergence and algorithmic information dynamics of systems and observers.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results.Frontiers in computational neuroscience · 2022Article
- A Review of Mathematical and Computational Methods in Cancer Dynamics.Frontiers in oncology · 2022Review
- Entropy as a Robustness Marker in Genetic Regulatory Networks.Entropy (Basel, Switzerland) · 2020Article
- Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.Frontiers in artificial intelligence · 2020Article
- Algorithmic Entropy and Landauer's Principle Link Microscopic System Behaviour to the Thermodynamic Entropy.Entropy (Basel, Switzerland) · 2018Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations. Here, we investigate the application of a simplicity bias based on a natural but algorithmic distribution of mutations (no recombination) in various examples, particularly binary matrices, in order to compare evolutionary convergence rates. Results both on synthetic and on small biological examples indicate an accelerated rate when mutations are not statistically uniform but
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